Energy efficient blow molder control

ABSTRACT

Blow molder system and associated method optimizes the performance, energy efficiency and/or operating costs of the blow molder. A blow molder controller executes a system model that relates blow molder input parameter changes to the characteristics of containers generated by the blow molder. Equipped with energy and/or operating cost data for operating the blow molder, the blow molder controller can select a set of blow molder input parameter changes for the blow molder that: drives the containers produced by the blow molder toward desired container characteristics, in an efficient amount of time, and in cost effective manner, considering the energy costs involved in implementing the changes.

PRIORITY CLAIM

The present application is a continuation of U.S. application Ser. No.18/128,715, filed Mar. 20, 2023, which is a continuation of U.S.application Ser. No. 17/361,614, filed Jun. 29, 2021, which is acontinuation of U.S. application Ser. No. 16/640,246, filed Feb. 19,2020, now U.S. Pat. No. 11,065,804, which is a national stageapplication under 35 U.S.C. § 371 of PCT application Serial No.PCT/US19/15564, filed Jan. 29, 2019, which PCT application claimspriority to U.S. provisional application Ser. No. 62/625,202, filed Feb.1, 2018, all having the same title and inventors as above.

TECHNICAL FIELD

The examples in this description and drawings generally relate tosystems and methods for operating a reheat, stretch blow molder controlsystem to improve energy efficiency.

BACKGROUND

Polyethylene terephthalate (PET) and other types of plastic containersare commonly produced utilizing a machine referred to as a reheat,stretch and blow molder. The blow molder receives preforms and outputscontainers. When a preform is received into a blow molder, it isinitially heated and placed into a mold. A rod stretches the preformwhile air is being blown into the preform causing it to stretch axiallyand circumferentially, and take the shape of the mold. A typical reheat,stretch and blow molder has between ten (10) and forty-eight (48) ormore molds. This increases the product rate of the blow molder, but alsoincreases the rate at which defective containers can be generated whenthere is a problem with one or more blow molding process parameters.Accordingly, container manufacturers are keen to detect and correct blowmolding process problems as efficiently as possible.

In the course of manufacturing blow-molded containers, it is desirableto control the blow molder to achieve desired container propertiesincluding, desired container dimensions, material distribution,strength, the absence of defects, etc. This is typically accomplishedmanually. According to one common technique, an operator of the blowmolder ejects a set of completed containers for off-line inspection.Various types of off-line inspections are used to measure differentaspects of the container. Material or thickness distribution is oftenmeasured using a qualitative “squeeze” test and/or a quantitativesection weight test. In a squeeze test, the operator, or other testingpersonnel, squeezes the container to obtain a qualitative indication ofwhether there is sufficient material at key locations of the container.In a section weight test, the container is physically divided intocircumferential sections. Each section is individually weighted,yielding the section weights. Other common off-line inspections includetop load and burst pressure tests to measure container strength,volumetric fill height and base clearance tests to measure containersize and shape, etc. Based on the qualitative and quantitative resultsof tests such as these, the operator modifies input parameters of theblow molder to move material to the appropriate locations within thebottle.

On-line inspection systems, such as the Intellispec™ product, availablefrom Pressco Technology Inc. of Cleveland Ohio and the PET-View product,available from the Krones Group of Neutraubling, Germany, utilizecomputer vision to inspect containers either in or downstream of theblow molder and reject mal-formed containers. These systems improve thequality of the container production by removing containers with randomlyoccurring damage, inclusions, and grossly formed containers, but havelimited success addressing process related issues that drive containerquality and performance.

Other inspection systems, such as the Pilot Profiler® infraredabsorption measurement devices available from AGR International ofButler, Pennsylvania, are capable of measuring the material distributionof individual containers. The measurements are made using a series ofemitters and sensors that are located either within or downstream of theblow molder. The sensors are oriented towards the sidewalls of thecontainers and generate measurements on the containers at 12.5 mmintervals, thus providing a profile of material distribution in thecontainer sidewalls. Also, advanced vision systems, such as the PilotVision™ system, also available from AGR International, Inc. of Butler,Pennsylvania, provide increased resolution and are able to detect moresubtle container defects.

Some existing systems utilize feedback from on-line container inspectionsystems to modify blow molder input parameters. For example, the SidelS.A.S. Company of Le Havre, France, has introduced a blow molder with amold control loop that to accommodate variations in the temperature ofperforms arriving at the mold. The mold control loop controls thepre-blow start and pre-blow pressure to detect changes in preformproperties and adapts the pre-blow pressure profile to account for anyvariations in preform energy or energy distribution.

Another process control system is the Process Pilot® product, availablefrom AGR International, Inc. of Butler, Pennsylvania. The Process Pilot®product is a closed loop control system used to manage the re-heatstretch and blow molding process. An infrared absorption-typemeasurement system, such as the Pilot Profiler® system described above,is used to generate a material distribution profile. The Process Pilot®product learns the relationship between the container blowing processand the location of the material in the container with a series ofautomated measurements made in conjunction with adjustments to the blowmolder input parameters. This information forms the basis for futureadjustments to the blow molder. A custom equation is used to express therelationship between blow molder input parameters and resulting materialdistributions. A control loop is implemented by establishing a baselinematerial distribution and baseline values for the various blow molderinputs. As the material distribution drifts during the blow moldingprocess, relationship between the blow molder input parameters andcontainer characteristics is utilized in conjunction with additionalmathematics to determine blow molder parameter values that minimize thedifference between the baseline and the measured material distributionwhile also minimizing control changes relative to baseline blow molderinput parameters. The Process Pilot® product can be operatedcontinuously to minimize the overall process variation.

SUMMARY

In one general aspect, the present invention is directed to a system andassociated method of operating a blow molder that optimizes theperformance, energy efficiency and/or operating costs of the blowmolder. A blow molder controller executes a system model that relatesblow molder input parameter changes to the characteristics of containersgenerated by the blow molder. Equipped with energy and/or operating costdata for operating the blow molder, the blow molder controller canselect a set of blow molder input parameter changes for the blow molderthat: drives the containers produced by the blow molder toward desiredcontainer characteristics, in an efficient amount of time, and in costeffective manner, considering the energy costs involved in implementingthe changes. These and other benefits realizable through embodiments ofthe present invention will be apparent from the description thatfollows.

FIGURES

Various embodiments are described herein by way of example inconjunction with the following figures, wherein:

FIG. 1 is a block diagram showing one embodiment of a blow moldersystem.

FIG. 2 is a block diagram of one embodiment of a blow molder controlsystem.

FIG. 3 illustrates one embodiment of a measuring device that may beassociated with the material distribution system.

FIG. 4 is a block diagram showing one embodiment of a base visionsystem.

FIG. 5 is a block diagram showing one embodiment of a sidewall visionsystem.

FIG. 6 is a block diagram showing one embodiment of a finish visionsystem.

FIG. 7 is a diagram showing example finish dimensions that may bemeasured utilizing the finish vision system.

FIG. 8 is diagram showing an image of the container illustrating variousmethods for determining clarity status.

FIG. 9 is a diagram showing one embodiment of a base temperature sensorsystem.

FIG. 10 is a diagram showing one embodiment of a birefringence sensorsystem for measuring crystallinity and/or orientation.

FIG. 11 is a diagram showing one embodiment of a near infrared (NIR)spectroscopy sensor system for measuring crystallinity.

FIG. 12 is a flow chart showing one embodiment of a process flow fortraining the system model.

FIG. 13 is a flow chart showing one embodiment of a process flow thatmay be executed by the blow molder controller to apply the system modelto generate sets of blow molder input parameter changes.

FIG. 14 is a flowchart showing one example of a process flow that may beexecuted by the operating cost module to generate sets of blow molderinput parameter changes.

FIG. 15 is a block diagram illustrating a computing device hardwarearchitecture, within which a set or sequence of instructions can beexecuted to cause a machine to perform examples of any one of themethodologies discussed herein.

DESCRIPTION

Various embodiments described herein are directed to systems and methodsfor improving the efficiency (e.g., energy efficiency) of a blow moldercontroller. A blow molder controller executes a system model thatrelates blow molder input parameter changes to the characteristics ofcontainers generated by the blow molder. In one embodiment, an operatingcost module of the blow molder controller evaluates sets of inputparameter changes generated using the system model in view of energyand/or other similar costs. The blow molder controller implements a setof blow molder input parameter changes that balances effectiveness andenergy cost efficiency.

Operators of blow molders want to lower their operating costs and alsobe more effective stewards of the environment. Operators andmanufacturers of blow molders may address this issue in several ways.For example, blow molder manufacturers have developed blow molders withmore efficient ovens and improvements to the way that high pressure airis managed. Also, blow molder input parameters may be managed in a waythat saves operating costs. Trends in container manufacturing towardslower resin weights for blow molded containers, however, makes itdifficult for blow molder operators to manage input parameters to saveoperating costs without also adversely affecting container properties.Further complicating the matter is the fact that each of the adjustableblow molder input parameters can have an asymmetrical impact onoperating costs. This makes it nearly impossible for an operator tomanually optimize the process for operating cost while maintainingcontainer performance.

Various blow molder controllers described herein are configured tomanage the blow molding process in a small operating window whilereducing the operating cost. A blow molder controller implements asystem model that relates the material distribution or containercharacteristics, such as thickness, crystallinity, etc., to particularblow molder input parameters. The blow molder controller monitors thecharacteristics of containers produced by the blow molder and adjuststhe blow molder input parameters according to the system model to drivethe container to a desired set of characteristics. The blow molder inputparameters may be operating condition changes to the operatingparameters of the blow molder (e.g., temperatures, pressures, etc.) thatcause the blow molder system to produce/form container with the desiredset of container characteristics. For example, the blow molder inputparameters can include a change to at least one of following operatingparameters of the blow molder system: the oven temperature, total ovenpower, individual oven lamp power, preform temperature set point,pre-blow start, pre-blow duration, stretch rod timing, blow pressure,pre-blow timing, pre-blow pressure, power levels for individual heaterelements of the plurality of molds, preform temperature set points,stretch rod timing, stretch rod temperature, blow pressure, etc.

The blow molder controller is programmed to use the system model togenerate multiple sets of input parameter changes in response to sensedcharacteristics of generated containers. Each set of input parameterchanges modifies the operation of the blow molder to move thecharacteristics of containers generated by the blow molder towarddesired (e.g., baseline) characteristic values, referred to herein asbaseline container characteristics. Different sets of input parameterchanges move the blow molder towards the baseline characteristics withdiffering levels of accuracy and/or time.

The blow molder controller also executes an operating cost module. Theoperating cost module receives operating cost data describing the costof electricity, heat, and/or other energy being consumed by the blowmolder. These data, e.g., the local electricity, heat and pressurizedair costs for the plant, may be input by an operator of the blow moldersystem 4 or a plant manager, for example, via a local or remote userinterface of the blow molder controller 102. Operating cost data, insome examples, also describes the effect that changing a blow molderinput parameter on one blow molder system can have on other blow moldersystems in the same plant. For example, plants including multiple blowmolder systems may generate high pressure air at one or more centralizedlocations and distribute the high pressure air to multiple blow moldersystem. A change to the pre-blow pressure, pre-blow timing,high-pressure blow pressure, high-pressure blow timing, or other highpressure air-related input parameters may have an effect on other blowmolder systems, for example, downstream on a common high pressure airmanifold. From these factors, the operating cost module determines theoperating cost associated with each of the set of input parameterchanges determined using the system model. The operating cost moduleselects a set of input parameter changes that balances accuracy andspeed with operating cost. That is, the blow molder control selects aset of input parameter changes that optimizes multiple factors,including (1) achieving or satisfying the desired containercharacteristics, (2) the time it takes to drive the containers to thedesired container characteristics, and (3) the operating costsassociated with implementing the blow molder input parameters to updatethe operating parameters of the blow molders to meet the desiredcontainer characteristics.

Before describing the blow molder controller in more detail, an overviewof a blow molder system is provided. FIG. 1 is a block diagram showingone embodiment of a blow molder system 4 according to variousembodiments. The blow molder system 4 includes a preform oven 2 thattypically carries the plastic preforms on spindles through the ovensection so as to preheat the preforms prior to blow-molding of thecontainers. The preform oven 2 may comprise, for example, infraredheating lamps or other heating elements to heat the preforms above theirglass transition temperature. Many blow molders 6 utilize preform ovensdefining multiple heating elements positioned to heat different portionsof the preforms. The preforms leaving the preform oven 2 may enter theblow molder 6 by means, for example, of a conventional transfer system 7(shown in phantom).

The blow molder 6 may comprise a number of molds, such as on the orderof ten to twenty-four, for example, arranged in a circle and rotating ina direction indicated by the arrow C. The preforms may be stretched inthe blow molder 6, using a fluid (e.g., air or a liquid) and/or a corerod, to conform the preform to the shape defined by the mold. In manyblow molders that use air to stretch the containers, an initial pre-blowis utilized to begin the container formation process followed by ahigh-pressure blow to push the now-stretched walls of the preformagainst the mold. Depending on the type of container to be generated,the molds may be heated (a hot mold process) or cooled (a cold moldprocess). Containers emerging from the blow molder 6, such as container8, may be suspended from a transfer arm 10 on a transfer assembly 12,which is rotating in the direction indicated by arrow D. Similarly,transfer arms 14 and 16 may, as the transfer assembly 12 rotates, pickup the container 8 and transport the container through the inspectionarea 20, where it may be inspected by one or more of the inspectionsystems described below. A reject area 24 has a reject mechanism 26 thatmay physically remove from the transfer assembly 12 any containersdeemed to be rejected. In some embodiments, the blow molder system 4 mayinclude alternate inspection areas.

In the example of FIG. 1 , the container 30 has passed beyond the rejectarea 24 and may be picked up in a star wheel mechanism 34, which isrotating in direction E and has a plurality of pockets, such as pockets36, 38, 40, for example. A container 46 is shown in FIG. 1 as beingpresent in such a star wheel pocket. The containers may then betransferred in a manner known to those skilled in the art to a conveyeror other transport mechanism according to the desired transport path andnature of the system. It will be appreciated that the blow molder system4 may comprise one or more inspection areas in addition to or instead ofthe inspection area 20. For example, alternate inspection areas may becreated by adding additional transfer assemblies, such as transportassembly 12. Also, alternate inspection areas may be positioned on aconveyor or other position down-line from the blow molder 6.

The blow molder system 4 may produce containers at a rate of 20,000 to120,000 per hour, though manufacturers continue to develop blow molderswith increasing speed and in some embodiments it may be desirable to runthe blow molder system 4 at lower rates. The blow molder system 4receives various inputs parameters that affect the characteristics ofthe generated containers. For example, the preform oven 2 may receive anoverall temperature input parameter, referred to as a preformtemperature set point, as well as additional input parameters thatdefine the distribution of heat between the individual heating elements.Other controllable parameters include, for example, a pre-blow timing, apre-blow pressure, etc.

FIG. 2 is a block diagram of one embodiment of a blow molder controlsystem 100. The system 100 comprises the blow molder system 4, a blowmolder controller 102, and various inspection systems 103. Theinspection systems 103 are positioned to sense characteristics ofcontainers produced by the blow molders. The inspection systems 103 maybe placed on-line to sense characteristics of the containers as they areproduced by the blow molder system 4, such as the inspection system 20in FIG. 1 . The blow molder controller 102 may comprise one or moreservers or other computer devices. The blow molder controller 102receives signals from the various inspection systems 103 indicatingcontainer characteristics and receives outputs from sensors from theblow molder system 4. The sensors of the blow molder system maycomprise, for example, an oven temperature sensor, a preform feed ratesensor, a timer for generating time stamps for when containers areblown, individual mold temperature sensors, perform temperature sensors,etc. As such, the blow molder controller 102 can receive data from theblow molder system 4 indicative the oven temperature, the preform feedrate, the timestamps for when containers are blown, individual moldtemperatures, preform temperatures, etc. The blow molder controller 102can also receive sensors from the plant in which the blow moldersystem(s) is housed, such as the ambient temperature, atmosphericpressure, and moisture in the plant. Based on these input data, the blowmolder controller 102 can generate blow molder input parameters orchanges thereto to cause the blow molder system 4 to generate containerswithin desired tolerances, as described herein below. The blow moldercontroller 102 may also references inputs from sensors related to theblow molder 4, as described herein. The blow molder controller 102comprises a system model 105 and an operating cost module 107, asdescribed herein.

Various different types of inspection systems 103 may be used. Forexample, a material distribution system 106 measures a materialdistribution profile of the container. According to various embodiments,the material distribution system 106 finds the material distribution ofcontainers after formation (e.g., either in or downstream of the blowmolder system 4). For example, the material distribution system 106 maybe used to take multiple direct or indirect readings of one or morecontainer characteristics across a profile (e.g., a vertical profile) ofthe container. The container characteristics may comprise, for example,wall thickness (e.g., average 2-wall thickness), mass, volume, etc.Material distribution may be derived from any of these measurements. Thesystem 106 may utilize measured container characteristics found acrossthe profile of the container to derive a material distribution of thecontainer. In some, but not all, embodiments, the measurements, andtherefore the calculated material distribution, need only be takenacross the oriented or stretched parts of the container and may excludenon-oriented portions of the container such as, for example, a finisharea, a base cup, etc. Calculations for converting raw measurements to amaterial distribution may be performed by on-board computing equipmentassociated with the system 106 and/or by the blow molder controller 102.

The material distribution system 106 may utilize any suitable type ofmeasurement device capable of measuring a material distribution profile.For example, FIG. 3 illustrates one embodiment of a measuring device 50that may be associated with the material distribution system 106. Themeasuring device 50 may be an in-line inspection system that inspectsthe containers as they are formed, as fast as they are formed, withouthaving to remove the containers from the processing line for inspectionand without having to destroy the container for inspection. Themeasuring device 50 may determine characteristics of each containerformed by the blow molder system 4 (e.g., average 2-wall thickness,mass, volume, and/or material distribution) as the formed containers arerotated or otherwise transported through an inspection area 21 followingblow molding. The inspection area 21 may be positioned similar to theexample inspection area 20 shown in FIG. 1 and/or at any other suitablein-line location, for example, as described above. Following blowmolding, containers, such as the container 66 in FIG. 3 , are passedthrough the inspection area 21 of the measuring device 50 by anysuitable mechanism such as, for example, a transfer assembly such as thetransfer assembly 12, a conveyor, etc.

As shown in FIG. 3 , the measuring device 50 may comprise two verticalarms 52, 54, with a cross bar section 56 there between at the lowerportion of the arms 52, 54. One of the arms 52 may comprise a number oflight energy emitter assemblies 60, and the other arm 54 may comprise anumber of broadband sensors 62 for detecting light energy from theemitter assemblies 60 that passes through a plastic container 66 passingbetween the arms 52, 54. Thus, light energy from the emitter assembly 60that is not absorbed by the container 66 may pass through the twoopposite sidewalls of the container 66 and be sensed by the sensors 62.The container 66 may be rotated through the inspection area 20 betweenthe arms 52, 54 by the transfer assembly 12 (see FIG. 1 ). In otherembodiments, a conveyor may be used to transport the containers throughthe inspection area 20.

According to various embodiments, the emitter assemblies 60 comprises apair of light emitting diodes (LED's), laser diodes, etc., that emitlight energy at different, discrete narrow wavelengths bands. Forexample, one LED in each emitter assembly 60 may emit light energy in anarrow band wavelength range where the absorption characteristics of thematerial of the container are highly dependent on the thickness of thematerial of the plastic container 66 (“the absorption wavelength”). Theother LED may emit light energy in a narrow band wavelength that issubstantially transmissive (“the reference wavelength”) by the materialof the plastic container 66. According to various embodiments, there maybe one broadband sensor 62 in the arm 54 for each emitter 60 in the arm52. Based on the sensed energy at both the absorption and referencewavelengths, the thickness through two walls of the container 66 can bedetermined at the height level of the emitter-sensor pair. Thisinformation can be used in determining whether to reject a containerbecause its walls do not meet specification (e.g., the walls are eithertoo thin or too thick). This information can also be used as feedbackfor adjusting parameters of the preform oven 2 and/or the blow molder 6(FIG. 1 ) according to various embodiments, as described further below.

The more closely the emitter-sensor pairs are spaced vertically, themore detailed thickness information, along the vertical profile of thecontainer, can be obtained regarding the container 66. According tovarious embodiments, there may be between three (3) and fifty (50) suchemitter-sensor pairs spanning the height of the container 66 from top tobottom. For example, there may be thirty-two emitter-sensor pairs spacedevery 0.5 inches or less, although additional emitter-sensor pairs maybe used, depending on the circumstances. Such closely spacedemitter-sensor pairs can effectively provide a rather complete verticalwall thickness profile for the container 66. In some embodiments withclosely spaced emitter-sensor pairs, adjacent emitter-sensor pairs maybe configured to operate at a small time offset relative to one anotherso as to minimize cross-talk.

According to various embodiments, when the measuring device 50 is usedto inspect plastic or PET containers 66, the absorption wavelengthnarrow band may be around 2350 nm, and the reference wavelength band maybe around 1835 nm. Of course, in other embodiments, different wavelengthbands may be used. As used herein, the terms “narrow band” or “narrowwavelength band” means a wavelength band that is less than or equal to200 nm full width at half maximum (FWHM). That is, the differencebetween the wavelengths at which the emission intensity of one of thelight sources is half its maximum intensity is less than or equal to 200nm. Preferably, the light sources have narrow bands that are 100 nm orless FWHM, and preferably are 50 nm or less FWHM.

The arms 52, 54 may comprise a frame 68 to which the emitter assemblies60 and sensors 62 are mounted. The frame 68 may be made of any suitablematerial such as, for example, aluminum. Controllers on circuit boards(not shown) for controlling/powering the emitter 60 and sensors 62 mayalso be disposed in the open spaces defined by the frame 68. Thecrossbar section 56 may be made out of the same material as the frame 68for the arms 52, 54.

The frame 68 may define a number of openings 69 aimed at the inspectionarea 20. As shown in FIG. 3 , there may be an opening for each sensor62. There may also be a corresponding opening for each emitter assembly60. Light energy from the emitter assemblies may be directed throughtheir corresponding opening into the inspection area 20 and toward thesensors 62 behind each opening 69. One example of a system such as thatdescribed above is set forth in U.S. Pat. No. 7,924,421 filed on Aug.31, 2007.

Another type of measuring device that may be used utilizes a broadbandlight source, a chopper wheel, and a spectrometer to measure the wallthickness of the container as it passes through an inspection areabetween the light source and the spectrometer after being formed by ablow molder. The broadband light source in such a system may providechopped IR light energy that impinges the surface of the plasticcontainer, travels through both walls of the container, and is sensed bythe spectrometer to determine absorption levels in the plastic atdiscrete wavelengths. This information may be used, for example, by aprocessor, to determine characteristics of the plastic bottle, such aswall thickness, material distribution, etc. In practice, such systemsmay use a thermal source to generate broadband light within the visibleand infrared spectrums of interest. The broadband light is chopped,collimated, transmitted through two walls of the plastic container, andfinally divided into wavelengths of interest by the spectroscope.Examples of similar systems are provided in U.S. Pat. No. 6,863,860,filed on Mar. 26, 2002, U.S. Pat. No. 7,378,047, filed on Jan. 24, 2005,U.S. Pat. No. 7,374,713, filed on Oct. 5, 2006, and U.S. Pat. No.7,780,898, filed Apr. 21, 2008. In yet other embodiments, the sensor(s)62 may be on the same side of the passing containers as the emitterassembly(ies) 60 and sense light that is reflected by the front and backsurfaces of the front sidewall of a passing container.

In various embodiments, the inspection systems 103 may also includevarious vision and other systems including, for example, a base visionsystem 108, a sidewall vision system 110 a finish vision system 112, abase temperature sensor system 114. Optionally, the inspection systems103 may also include sensor systems for directly measuringcrystallinity. For example, a birefringence sensor 115 may measurecrystallinity in cold mold-generated containers. A near infrared (NIR)spectroscopy sensor may measure crystallinity in hot mold-generatedcontainers. Any or all of the various inspection systems 103 may beconfigured to operate in-line and inspect the containers as they areformed, as fast as they are formed, without having to remove thecontainers from the processing line for inspection and without having todestroy the container for inspection.

The vision system or systems may be similar to the vision system used inthe infrared absorption measurement devices available from AGRInternational, Inc. of Butler, Pennsylvania, or as described in U.S.Pat. No. 6,967,716, filed on Apr. 21, 2000. FIG. 4 is a block diagramshowing one embodiment of a base vision system 108. The system 108comprises a camera 202, optics 204, a light source 208 and an optionalimage processor 210. Images may be taken while the container 66 is inthe inspection area 21, with the container 66 positioned verticallybetween the lower light source 208 and the upper/overhead camera 202.Resulting images may be useful, as described herein below, fordetermining the presence of haze or pearlescence in the container 66.Images from the camera 202 may be provided to an image processor 210,which may perform various pre-processing and/or evaluate the images todetermine properties of the container 66 such as, clarity status (e.g.,haze or pearlescence status), (various container dimensions, etc.).Examples of systems for determining the clarity status of blow-moldedcontainers are provided in U.S. Pat. No. 9,539,756, issued Jan. 10,2017. In some embodiments, the image processor 210 is omitted and imageprocessing is performed by the blow molder controller 102. In theembodiment shown in FIG. 4 , the camera 202 and optics 204 arepositioned above the container 66. The optics 204 may include variouslenses or other optical components configured to give the camera 202 anappropriate field of view 206 to sense the base area 66 a of thecontainer 66 through the finish 66 b. It will be appreciated that otherconfigurations of the base vision system 108 are also possible. In someembodiments, the positions of the camera/optics 202/204 and light source208 may be reversed. Also, in some embodiments, additional cameras (notshown) having additional fields of view may be utilized.

FIG. 5 is a block diagram showing one embodiment of a sidewall visionsystem 110. The illustrated example sidewall vision system 110 comprisestwo cameras 214, 214′, two optics assemblies 216, 216′ a light source212 and the optional image processor 210′. Images may be taken while thecontainer 66 is in the inspection area 21, with the container positionedbetween the light source 212 and the cameras 214, 214; that is, thelight source 212 and cameras 214, 214′ are positioned on opposite sidesof the container 66 as shown in FIG. 5 As illustrated, the two cameras214, 214′ and optics 216, 216′ are configured to generate respectivefields of view 218, 218′ that show sidewall regions 66 c of thecontainer 66. The image processor 210′ may perform various processing onimages generated by the camera 214 including, for example, detectingcontainer defects, detecting the clarity status (e.g., haze orpearlescence status) of the container, etc. In some embodiments, theimage processor 210′ performs pre-processing on images generated by thecamera 214, with further processing performed directly by the blowmolder controller 102. Also, in some embodiments, the image processor210′ may be omitted altogether. Also, in some embodiments, one or moreof the cameras 214, 214′ may be omitted and/or additional cameras withadditional fields of view (not shown) added.

FIG. 6 is a block diagram showing one embodiment of a finish visionsystem 112. The illustrated example finish vision system 112 comprises acamera 220, optics 222, light sources 224, 226, and the optional imageprocessor 210″. Images may be taken while the container 66 is in theinspection area 21, with the container positioned between the lightsources 224, 226 and the camera 220, such that the light source 226 andthe camera 220 are positioned on opposite sides of the container 66 andsuch that the light source 224 is above the container 66. Asillustrated, the camera 220 and optics 222 are configured to generate afield of view 225 that includes the finish area 66 b of the container66. In some configurations, the finish vision system 112 comprises abacklight source 226 positioned in the field of view 225 to illuminatethe finish 66 b. Also, in some embodiments, the finish vision system 112comprises a round or bowl shaped light source 224 positioned above thefinish 66 b. An image processor 210″ may perform various processing onimages including, for example, deriving from the images variouscontainer characteristics (e.g., dimensions, clarity status, etc.). Someor all of the image processing, however, may be performed by the blowmolder controller 102 and, in some embodiments, the image processor 210″may be omitted. FIG. 7 is a diagram showing example finish dimensionsthat may be measured utilizing the finish vision system 112. Forexample, the dimension H indicates a height of the finish. A dimension Aindicates a total width of the finish 66 b. A dimension T indicates awidth of the threads 66 e of the container 66. A dimension E indicates awidth of the seal 66 f of the finish.

It will be appreciated that the various vision systems 108, 110, 112 maybe embodied by any suitable type of system capable of generating imagesof the desired portions of the containers 66. For example, the base andsidewall vision systems 108, 110 may be implemented utilizing the PilotVision™ system, available from AGR International, Inc. of Butler,Pennsylvania. The finish vision system 112 may be implemented utilizingthe Opticheck™ system, also available from AGR International, Inc. ofButler, Pennsylvania. It will further be appreciated that images fromadditional perspectives may be obtained by positioning cameras and lightsources at different locations, for example, within the inspection area20 or downstream of the blow molder system 4.

In some embodiments, outputs of the various vision systems 108, 110, 112are utilized to determine the presence of haze or pearlescence,generally referred to herein as a clarity status. Processing todetermine the clarity status of containers may be performed by the blowmolder controller 102 and/or by any of the various image processors 210,210′, 210″ described herein. Any suitable image processing algorithm maybe utilized to determine haze or pearlescence status (e.g., the claritystatus) of a container. For example, FIG. 8 is diagram showing an image240 of the container 66 illustrating various methods for determiningclarity status. The image 240 is comprised of a plurality of pixels,where each pixel has a value. For example, when the image 240 is agray-scale image, each pixel may have a value indicating the brightnessof the image at the location of the pixel. When the image is a colorimage, the value of each pixel may indicate color as well as brightness.In FIG. 8 , the emphasis area 242 is reproduced in larger form toillustrate image pixels 243. Gray-scale values for various pixels areindicated by shading. In practice, the blow molder controller 102, orother suitable processor, may identify instances of haze or pearlescenceby examining the images for anomalous pixels. Anomalous pixels may bepixels having a gray-scale or other value that is different from theexpected value, for example, indicating that the container 66 is darkerthan expected. Anomalous pixels may be identified in any suitablemanner. For example, anomalous pixels may be darker than a thresholdvalue and/or greater than a threshold amount darker than the average ofall pixels making up the bottle. Pearlescence or haze may be detected,for example, by identifying a total number of anomalous pixels in thearea representing the container 66 and/or a portion thereof (e.g., abase portion). Also, in some embodiments, a size and or number ofcontiguous groupings of anomalous pixels, such as grouping 244, may beutilized. Results of the algorithm may be expressed in a binary manner(e.g., pearlescence or haze is present; pearlescence or haze is notpresent) or in a quantitative manner, for example, based on the numberof anomalous pixels or pixel groupings.

FIG. 9 is a diagram showing one embodiment of a base temperature sensorsystem 114. The system 114 may comprise a temperature sensor 230positioned with a field of view 232 that includes the base 66 a of thecontainer 66. The temperature of the base 66 a of the container 66 maybe taken while the container 66 is in the inspection area 21, with thecontainer 66 positioned in the field of view of the temperature sensor230. The temperature sensor 230 may comprise any suitable non-contact orinfrared sensor including, for example, any suitable pyrometer, aninfrared camera, etc. Signals from the sensor 230 may be provided to theblow molder controller 102 and/or another suitable processor forderiving a base temperature from the signals. It will be appreciatedthat various other temperature sensors may be utilized including, forexample, a sidewall temperature sensor (not shown) with a field of viewdirected at the sidewall area 66 c of the container 66.

FIG. 10 is a diagram showing one embodiment of a birefringence sensorsystem 115 for measuring crystallinity and/or orientation. Birefringenceis an effect found in many materials, including PET. In someembodiments, a birefringence sensor system 115 may be utilized inconjunction with cold mold-generated containers to measure crystallinity(or orientation) expressed as bi-axial lattice structure. Birefringenceoccurs when linearly polarized light with two orthogonal componentstravel at different rates through a material. Because the orthogonalcomponents travel at different rates through the container 66, there isa resulting phase difference between the two light components. Thedifference in the rates of travel of the light components, and thereforethe observed phase difference, depends on the level of crystallinityexhibited by the container. For example, one component may be consideredthe fast beam and the other a slow beam. The difference in rate, andtherefore phase, is measured as retardance, which is the integratedeffect of birefringence acting along an optical path in a material.Retardance is often measured according to a unit of (nm/cm thickness).Retardance can also be expressed as a phase angle when considering thewavelength of light used.

To measure retardance, a birefringence sensor system 115 may transmitlinearly polarized light through the container 66. The system 115 maycomprise an illumination source 250, a polarizer 252, and a sensor 254.Measurements of crystallinity may be taken while the container 66 is inthe inspection area 21, which may be positioned between the illuminationsource 250 and the sensor 254. For example, the illumination source 250and polarizer 252 may be positioned on one side of the container 66 andconfigured to illuminate the container 66. The sensor 254 may bepositioned on a side of the container 66 opposite the illuminationsource 250 and polarizer 252 and may be configured to receive theillumination provided by the illumination source 250. The polarizer 252may be oriented to cause illumination directed towards the container 66to be linearly polarized with two orthogonal components. For example,the polarizer 252 may comprise two polarizer elements, 252 a, 252 boriented orthogonal to one another about an optical axis 251. In someembodiments, the orientation of the linear polarizer 252 may be rotatedabout 45° relative to the axis of crystallization of the container 66. Asensor 254 opposite the source may receive the light, including the twoorthogonal components. In some embodiments, an optional electricallycontrolled liquid crystal variable polarization device 256 or equivalentthat filters the light is placed between the container 66 and the sensor254. The variable polarization device 256 may be modified to allow thesensor 254 to alternately sense the two formerly orthogonal componentsof the incident beam and thereby measure the phase difference and/ordifference in rate. For example, the angle difference between thepositions of the variable polarization device 256 when measuring the twoformerly orthogonal components may be proportional to the phasedifference. The amount of phase difference per unit thickness of thecontainer walls is the retardance. Accordingly, the end result may be afunction of crystallinity and the thickness of the material. Forexample, the blow molder controller 102 may utilize container thickness(e.g., as measured by the material distribution system 106) to back-outa quantitative measurement of container crystallinity. Although thesystem 115 is illustrated in a configuration that directs theillumination through the sidewall regions 66 c of the container 66, thesystem 115 may be configured to measure birefringence through anysuitable portion of the container 66. In some embodiments, the sensorsystem 115 also comprises a processor 258. The processor 258 may, forexample, process the output of the sensor 254 to generate acrystallinity reading for the container 66. In embodiments including thevariable polarization device 256, the processor 258 may also be incommunication with the variable polarization device 256 to control itspolarization value. In various embodiments, some or all of thesefunctionalities may be executed by the blow molder controller 102. Forexample, the processor 258 may be omitted. Also, any suitable method orapparatus may be used for measuring birefringence or retardance.Examples of suitable methods and apparatuses for measuring birefringenceor retardance may be found in the following sources, which areincorporated herein by reference in their entireties: Hagen, et al.,“Compact Methods for Measuring Stress Birefringence;” Ai et al.,“Testing stress birefringence of an optical window,” SPIE Vol. 1531Advanced Optical Manufacturing and Testing II (1991); Dupaix et al.,“Finite strain behavior of poly(ethylene terephthalate) (PET) andpoly(ethylene terephthalaate)-glycol (PETG), Polymer,” Vol. 46, Iss. 13,pgs. 4827-4838 (17 Jun. 2005); and U.S. Pat. No. 5,864,403, filed onFeb. 23, 1998.

FIG. 11 is a diagram showing one embodiment of a near infrared (NIR)spectroscopy sensor system 117 for measuring crystallinity. The system117 may be positioned in the inspection area 20 of the blow moldersystem 4 and/or downstream of the blow molder system 4. In someembodiments, a NIR spectroscopy sensor system 117 may be used inconjunction with hot mold-generated containers to measure crystallinityexpressed as spherulitic structure. The system 117 comprises anillumination source 260 positioned on one side of the container 66 and aspectrometer 262 positioned on another side of the container 66 oppositethe container 66. The illumination source 260 and spectrometer 262 maybe configured to measure absorption through the container 66 over all ora portion of the near infrared spectrum. For example, the illuminationsource 260 and spectrometer 262 may measure absorption across awavelength range of 800 nm to 3000 nm. In some embodiments, theillumination source 260 and spectrometer 262 may measure absorptionacross a wavelength range of 2000 nm to 2400 nm.

The illumination source 260 and spectrometer 262 may be tuned to aparticular wavelength or wavelength range in any suitable manner. Forexample, the illumination source 260 may be a broadband sourcegenerating illumination across the desired wavelength range. Thespectrometer 262 may be configured to measure the intensity of theillumination (e.g., after transmission through the container 66) atdifferent wavelengths. For example, the spectrometer 262 may comprise adiffraction grating 266 or other suitable optical device for separatingreceived illumination by wavelength across the desired range (e.g.,spatially separating the received illumination by wavelength). Acontrollable micromirror 268 or other similar device may direct aportion of the spatially separated illumination corresponding to awavelength or wavelength range to a sensor 269, such as an InGaAsdetector. The sensor 269 may provide an output signal proportional tothe intensity of the received illumination at the wavelength orwavelength range directed to the sensor 269 by the micromirror 268. Themicromirror 268 may be progressively tuned to direct differentwavelengths or wavelength ranges to the sensor 269, providing a set ofsignals from the sensor 269 that indicate absorption of the illuminationby the container 66 over the desired wavelength range. This may bereferred to as an absorption spectrum or spectrum for the container 66.For example, the amount of illumination that is transmitted by thecontainer 66 at any given wavelength may be the inverse of theabsorption of the container 66 at that wavelength.

A processor 264 may be configured to control the micromirror 268 and/orreceive and store signals from the sensor 269 to determine theabsorption spectrum for the container 66. In some embodiments, some orall of the functionality of the processor 264 may be performed by theblow molder controller 102. For example, the processor 264 may beomitted. Also, although the illumination is shown to intersect thecontainer 66 at the sidewall region 66 c, the absorption spectrum may betaken at any suitable portion of the container 66. Also, FIG. 11 showsjust one example spectrometer 262. Any suitable type of spectrometer maybe used.

Referring back to FIG. 2 , the blow molder controller 102 receivescontainer characteristic data from one or more inspection systems 103,outputs from sensors of the blow molder system 4, and/or output fromsensors from the plant. The container characteristic data describescontainers generated by the blow molder system 4. The sensor outputsfrom the blow molder sensors describe internal operating conditions ofthe blow molder system 4 as described above, such as the oventemperature, the preform feed rate, time stamps for blowing of thecontainers such that time lapses since the last blowing can bedetermined, individual mold temperatures, perform temperatures, etc. Thedata from the plant sensors can comprise the ambient plant temperature,pressure and moisture, for example. Based on the containercharacteristic data, the blow molder controller 102 generates sets ofblow molder input parameter changes that, if applied, would movecontainers generated by the blow molder system 4 towards the baselinecontainer characteristics.

In some examples, inputs provided to the system model 105 by the blowmolder controller 102 include container characteristics described by thecontainer characteristic data including, for example, container materialdistribution, clarity status, thickness, etc. The clarity status mayindicate a haze status in a hot mold process or a pearlescence status ina cold mold process. In addition to or instead of the clarity status,the system model 104 may receive a direct measurement of containercrystallinity. Based on the inputs, the system model 105 produces setsof values for blow molder input parameters. These sets of inputparameters are provided to the operating cost module 107, as describedherein.

The system model 105 may be any suitable type of model and may begenerated in any suitable manner. For example, the system model 105 mayutilize a strong R² correlation between the container characteristicsand the blow molder input parameters. The system model 105 may beimplemented and trained in any suitable manner. For example, FIG. 12 isflow chart showing one embodiment of a process flow 1200 for trainingthe system model 105. The process flow 1200 may be executed, forexample, by the blow molder controller 102. At 1202, the blow moldercontroller 102 measures characteristics of containers generated by theblow molder system 4. For example, the material distribution may bemeasured in conjunction with the material distribution system 106. Theclarity status may be measured in conjunction with one or more of thevision systems 108, 110, 112. The crystallinity may be measured by thebirefringence sensor system 115 and/or the NIR spectroscopy system 117.In some embodiments, the operation of the blow molder system 4 is tuned(e.g., manually) prior to measuring the one or more containers such thatthe material distribution and clarity status of the measured containersis correct. Accordingly, the measured containers may establish abaseline material distribution, clarity status, and/or crystallinitystatus for the model, referred to collectively as the baseline containercharacteristics. The blow molder controller 102 may also establish abaseline set of blow molder input parameters that bring about thebaseline container characteristics, at least in view of the blow moldersystem environment at the time that the baseline is determined.

In some embodiments, additional tuning may be performed relative to thecrystallinity and clarity status before taking the baselinecharacteristic measurements at operation 1102. In a cold mold process,for example, the blow molder controller 102 may decrease the preformtemperature set point until pearlescence appears (e.g., until theclarity status indicates that pearlescence is present). Then the blowmolder controller 102 may increase the preform temperature set pointuntil pearlescence is no longer present. Subsequently, the controlsystem may take the baseline measurements at 1102. Similarly, for a hotmold process, the blow molder controller 102 may increase the preformtemperature set point until haze appears (e.g., until the clarity statusindicates that haze is present). Then the blow molder controller 102 maydecrease the preform temperature set point until pearlescence is nolonger present before taking the baseline measurements at 1102. This mayensure that the baseline measurements for the system model 105 are takenwith crystallinity at or near its optimal value. Also, in variousembodiments, the blow molder controller 102 may be programmed toperiodically perform the described clarity tuning during operation ofthe blow molder system 4. This may correct for process drift, which maytend to push the blow molder system 4 away from generating containers atoptimal crystallinity. In some embodiments, the baseline measurements at1102 may be taken with the blow molder system 4 tuned to generatecontainers with small, but acceptable, levels of haze or pearlescence.This may drive the system model 105 to generate containers with optimalcrystallinity, as described herein.

At operation 1204, the blow molder controller 102 records (e.g., storesin memory) the container characteristics of some or all of the containergenerated along with values of the blow molder operating parameters forthe blow molder system 4 at the time that each container was produced.These values may be entered into a multi-dimensional matrix that may beused, for example, as described herein below.

At operation 1206, the blow molder controller 102 may generates a systemmodel 105 relating blow molder input parameters and containercharacteristics. For example, the blow molder controller 102 may utilizethe matrix to derive the model of blow molder system 4 parameters versusresulting container characteristics. The system model 105 may begenerated using any suitable technique or techniques. Example modelingtechniques that may be used include, for example, linear regressionmethods, stepwise regression, principle components regression, etc. Insome embodiments, the relationship between blow molder input parametersand material distribution indicated by the model is a relationshipbetween desired changes in material distribution and correspondingchanges in blow molder input parameters.

Optionally, the model may be tested or validated upon generation atoperation 1206. If the model validates, then the model generation may becomplete at operation 1207. If the model fails to validate, the blowmolder controller 102 may modify the blow molder input parameters atoperation 1208 and generate new containers at operation 1210. The modelmay fail to validate, if the model generates blow molder inputparameters that are out of an acceptable range, or the characteristicsof the containers generated during the actions operation 1202, operation1204 do not represent acceptable baseline container characteristics,etc. The blow molder controller 102 may measure and/or derive thecontainer characteristics at operation 1202, record (e.g., store inmemory) the container characteristics 302 and new blow molder inputparameters 306 at 1604 (e.g., to the multi-dimensional matrix) anddetermine, again, if the system model 105 validates at operation 1206.In some embodiments, this process is repeated until the system model 105validates.

Once the system model 105 is generated, it may be used to generate setsof blow molder input parameters to drive containers to the baselinecontainer characteristics, as described herein. For example, FIG. 13 isa flow chart showing one embodiment of a process flow 1300 that may beexecuted by the blow molder controller 102 to apply the system model 105to generate sets of blow molder input parameter changes. At operation1302, the blow molder controller 102 receives container characteristicdata. In some examples, the blow molder controller 102 processes some orall of the container characteristic data to derive containercharacteristics such as, material distribution, clarity status,crystallinity, etc.

At operation 1304, the blow molder controller 102 determines if one ormore of the container characteristics from operation 1302 are more thana threshold from the baseline container characteristics. If not, theblow molder controller 102 returns to operation 1302 and receives nextcontainer characteristic data.

If one or more of the container characteristics is more than a thresholdfrom the baseline container characteristics, the blow molder controller102 utilizes the system model 105 to generate sets of blow molder inputparameters at operation 1304. For example, the blow molder controller102 may calculate an error signal representing a difference between thecontainer characteristics of generated containers received and/orderived at operation 1302 and the baseline characteristics measuredduring model training, as described with respect to FIG. 12 . The errorsignal represents a desired change in the container characteristicsgenerated by the blow molder system 4.

The error signal is applied to the system model 105, which may returnchanges that can be made to the blow molder system 4 input parameters tobring about the desired changes and drive the container characteristicsback to the baseline. For example, utilizing the relationship betweencontainer characteristics and blow molder input parameters, the blowmolder controller 102 may derive sets of blow molder input parametersthat minimize the difference between the container parameters and thebaseline container parameters (e.g., the error signal).

In one embodiment, the blow molder controller 102 generates multiplesets of changes to blow molder input parameters and, in some examples, ascore for each set. A set of changes to blow molder input parametersincludes one or more changes to the blow molder input parameters. Thescore for a set of changes to blow molder input parameters describes theeffectiveness of the set such as, for example, how quickly and howeffectively the changes will bring the container characteristicsconsistent with the baseline.

As described above, the initial baseline material distribution may bebased on the containers measured to generate the model. In someembodiments, the model and/or an additionally generated model, may beused to correlate material distribution values to section weights, forexample, as described in co-pending U.S. Patent Application PublicationNo. 2012-0130677, filed on Nov. 18, 2011.

At operation 1308, the blow molder controller 102 (e.g., the operatingcost module 107 thereof) selects a set of blow molder input parameters,for example, from the sets of blow molder input parameter changesderived at operation 1306. The selected set of blow molder inputparameters balances effectiveness with operating cost. Any suitableoptimization method may be used. For example, the operating cost module107 may assign an operating cost to each blow molder parameter change inthe sets of blow molder parameter changes. The operating cost for a blowmolder parameter change may be found, for example, as described hereinwith respect to FIG. 14 . The operating cost module 107 selects the setof blow molder parameter changes that minimize the operating cost andthe effectiveness score.

In some examples, the operating cost for a blow molder parameter changeor set of parameter changes considers the downstream line efficiencycost. The downstream line efficiency cost may be mined from datacollection systems at the plant and may describe the effect of a changein a blow molder input parameter (such as a high pressure air-relatedparameter) on other blow molder systems in the plant.

In some examples, the operating cost module 107 may also consider anexpected scrap rate. For example, the efficiency score for a set of blowmolder parameter changes may include a component indicating the timeuntil the blow molder system 4 reaches the baseline. Longer times mayindicate higher scrap rates. The operating cost module 107, in someexamples, selects a set of blow molder parameter changes to minimize thescrap rate as well as the operating cost.

In some examples, the operating cost module 107 also considers a stateof the blow molder system 4 and/or the state of the plant, including,for example, current oven temperature, preform feed rate, time sincelast blowing container, ambient plant temperature, mold temperatures,preform temperature, etc. These data may be input from sensors of theblow molder system 4 and/or the plant, as described above. For example,the incremental cost of blow molder input parameters may depend on thecurrent state of the blow molder system 4, as described herein. Theoperating cost module 107 may determine the operating cost of each ofthe sets of blow molder input parameters by finding an operating costfor each change of the blow molder input parameters in the sets ofchanges determined using the system model 105.

FIG. 14 is a flowchart showing one example of a process flow 1400 thatmay be executed by the operating cost module 107.For example, theoperating cost module 107 may execute the process flow 1400 to determinethe incremental cost of changing various blow molder input parameters.At operation 1402, the operating cost module 107 receives operating costdata describing the cost of energy at the blow molder system 4. Thesedata may be input to the blow molder controller 102 via a user or datainterface as described above. The energy cost data depend, for example,on the operating costs at the location where the blow molder system 4 isoperating. The cost of energy, for example, describes a cost ofelectricity from an electric grid. Electricity from the grid may be usedto power blow molder ovens, generate compressed air for blowingcontainers, etc. In some examples, some or all of the energy foroperating the blow molder is from generators, pumps, or other componentspowered by internal combustion engines, solar panels, etc. When some orall of the energy for powering the blow molder system 4 is derived fromsources of this type, the cost of energy may include the cost of dieselor other fuel, maintenance costs for generators, solar panels, etc.

At operation 1404, the operating cost module 107 receives and/oraccesses plant data describing the arrangement of the plant where theblow molder system 104 is operated. Plant data may include, for example,data describing a system for generating and distributing pressurizedair. For example, generating and distributing high pressure air to blowmolders may consume a significant portion of the energy consumed by ablow molder. Data describing the system for generating and distributingpressurized air may include, for example, data describing theefficiencies of the air compressors, data describing the number ofblow-molders at the plant being supplied with high pressure air, datadescribing the layout of the high pressure air manifold within theplant, etc.

At operation 1406, the operating cost module 107 receives and/oraccesses the blow molder operating parameters that are available formodification by the blow molder controller 102. Blow molder operatingparameters that may be modified by the blow molder controller 102 mayinclude, for example, total oven power, individual oven lamp power,preform temperature set point, pre-blow start, pre-blow duration,stretch rod timing, blow pressure, etc.

At optional operation 1408, the operating cost module 107 receivesand/or accesses data describing a state of the blow molder system 4 suchas, for example, current oven temperature, preform feed rate, time sincelast blowing container, ambient plant temperature, mold temperatures,preform temperature, and desired preforms rejected prior to start. Insome examples, the operating cost of various changes to blow molderinput parameters is dependent on the current state of the blow moldersystem 4.

At operation 1410, the operating cost module 107 determines the cost ofincremental changes to the blow molder input parameters that areavailable for modification in view of the operating cost received atoperation 1402. For example, the operating cost of increasing the blowpressure and/or increasing the duration of the pre-blow may be foundconsidering the cost of electricity, the efficiency of the compressor orcompressors for generating high pressure air, and an efficiency of themanifold system for providing high pressure air to the blow moldersystem 4. The operating cost of increasing an oven temperature settingmay be found by accessing or determining an incremental change in thepower drawn by the oven in view of the change. In some examples, theoperating cost module 107 also considers reductions in energyconsumption caused by blow molder input parameter changes that, forexample, lower blow pressure, reduce the duration of the pre-blow,lowering the temperature at an oven, etc.

In some examples, where the operating cost of incremental changes to theblow molder parameters depends on blow molder system and/or plantconditions, the operating cost module 107 may re-execute all or parts ofthe process flow 1400 under different blow molder system and/or plantconditions and recalculate incremental costs of changes to blow moldersystem parameters.

In other embodiments, instead of determining multiple sets of blowmolder input parameters and then computing the incremental costs ofimplementing each set in order to determine the set that is output tothe blow molder system, the blow molder controller 102 could incorporatethe costs of making the incremental changes into the system model matrixthat relates blow molder input parameters and container characteristics.For example, blow molder input parameters that have a high relative costwould be weighted lower than blow molder input parameters that have alower relative cost. For example, if either an oven temperature increaseor a blow pressure increase will cause a desired effect in containercharacteristics, and at a particular plant location it is significantlymore expensive to make the necessary increase in blow molder oventemperature than to make the necessary change the blow pressure, therespective matrix weightings for oven temperature increase and blowpressure increase can cause the optimization of matrix parameters by thesystem model 105 to determine that blow pressure should be increasedrather than increasing the oven temperature, without having to generatemultiple sets of blow molder input parameter changes at step 1306 ofFIG. 13 , for example.

The systems and methods herein may be used across a variety ofproduction conditions for the blow molder system 4. In some examples,the systems and methods described herein are useful dealing withstep-wise changes, slower environment changes, and start-up. Step-wiseprocess changes cause the blow molder system 4 to vary both quickly andover long periods of time. For example, when a new batch or gaylord ofpreforms is dumped into the descrambler for the blow-molder, differencesin resin chemical properties and the temperature of the preforms causesa step-wise change in the process that can immediately result in theproduction of bad containers. Each time a new batch of preforms isadded, the process can jump.

In some examples, in response to a step-wise process change, the blowmolder controller 102, using the system model 105, would return sets ofchanges to blow molder parameters that would add or subtract ovenenergy, modify, global mold control parameters such as pre-blow start,pre-blow duration, stretch rod timing, and/or blow pressure etc. Theoperating cost module 107 may consider how the change will affect thequality of the blown container, (e.g. the effectiveness scores generatedusing the system model 105) and the marginal cost impacts of the sets ofchanges to blow molder parameters.

The blow molder controller 102 may also report energy correlated usageor efficiency based on individual molds, spindles or heaters. Reportingblow molder elements that are more costly than other similar elementswould be useful to monitor operating costs. For example, if one preformheating element is costing more than another to operate, or one mold ismore costly to heat than another, or one pressure line has to be sethigher than the others and may becoming blocked. This could add elementsof a replacement needed, give guidance for preventative maintenance andintroduce predictive diagnostics. Eliminating unplanned downtimeenhances efficiency and improves operating costs.

The blow molder controller system 102 as described herein may also beuseful in the event of condition drift while the blow molder system 4 isin use. For example, in some plants, the ambient air temperature candrift up and down by 10 to 20C. This results in slowly changingcontainer quality that may first appear at the worst performing molds.The time-to-detect determines how many bad containers are produced.Making matters worse, when detected, the changes implemented by theoperator will effectively unwind the process and do so without regard toits overall cost. As with step-wise events, the slowly moving changesthat affect container quality along with the cost of the process can beautomatically adapted and consistently adjusted to obtain qualitycontainers for an optimized cost value.

In other embodiments, the cost model may choose to place limits oncontrols that are possible but require higher than allowed expense. Forinstance, a higher pressure may be achievable but prolonged use maycause long term effects that will result in unplanned down time oroutages. There may also be periods of time that energy usage is limiteddue to rolling brownouts or peak energy usage that the cost model mayconsider and choose to throttle back production. It would also bepossible to slow production and energy usage to meet budgetaryconstraints at the facility.

The blow molder controller system 102 as described herein may also beuseful upon startup. For example, each time the blow molder system 4pauses due to a downstream container flow issue, there may be cooling inthe ovens. The amount of cooling depends on the length of time theblower is paused. This can create a different startup condition eachtime. When re-starting a blow molder system 4 after it has been down forsome time, the most efficient blow molder input parameter changes mayalso be the costliest. The operating cost module 107 may reduce theoperating cost incurred by a blow molder system 4 on startup. In someexamples, the blow molder controller 102 may execute the process flow1400 when a startup is detected to determine incremental costs ofchanging blow molder parameters on different blow molder 4 conditions.

In one general aspect, therefore, the present invention is directed to ablow molder system 4 comprising a blow molder 6 that produces containersfrom preforms. The blow molder comprises a plurality of molds and a blowmolder sensor for sensing an operating condition of the blow molder. Theblow molder system 4 also comprises a container inspection system 20 forinspecting the containers produced by the blow molder. In addition, theblow molder system comprises a blow molder controller 102 that is incommunication with the blow molder and the container inspection system.The blow molder controller is configured to: (a) receive outputs fromthe blow molder sensor and the container inspection system; (b)determine a set of blow molder input parameters for the blow molder thatdrives the containers generated by the blow molder toward a desiredcontainer characteristic, wherein the set of blow molder inputparameters are determined based on: (i) the outputs from the containerinspection system and blow molder sensor; and (ii) operating cost datafor the blow molder, wherein the operating cost data comprises energycosts for operating the blow molder; and (c) output the set of blowmolder input parameters to the blow molder for implementation by theblow molder.

In another general aspect, the present invention is directed to a methodcomprising the steps of producing, by a blow molder, blow-moldedcontainers from preforms and, during production of the blow-moldedcontainers, sensing, by a blow molder sensor, an operating condition ofthe blow molder. The method further comprises the step of inspecting, bya container inspection system, the blow-molded containers produced bythe blow molder. In addition, the method comprises the step ofdetermining, by a blow molder controller that is in communication withthe blow molder and the container inspection system, a set of blowmolder input parameters for the blow molder that drives the containersgenerated by the blow molder toward a desired container characteristic,wherein the set of blow molder input parameters are determined based on:(i) outputs from the container inspection system and blow molder sensor;and (ii) operating cost data for the blow molder, wherein the operatingcost data comprises energy costs for operating the blow molder. Themethod further comprises the step of outputting, by the blow moldercontroller, the set of blow molder input parameters to the blow molderfor implementation by the blow molder. In addition, in variousembodiments, the method may further comprise the step of, afteroutputting the set of blow molder input parameter to the blow molder,producing, by the blow molder, blow-molded containers with the set ofblow molder input parameters from the blow molder controller. Inaddition, the method may further comprise the steps of inputting, by auser via a user interface of the blow molder controller, and receivingby the blow molder controller via the user interface, the operating costdata.

In various implementations, the blow molder controller determines theset of blow molder input parameters that optimizes a plurality offactors, where the plurality of factors comprise satisfaction of thedesired container characteristic; time to reach the desired containercharacteristic; and operating costs for the blow molder to implement theset of blow molder input parameters, wherein the operating costs arebased on the operating cost data for the blow molder. In addition, theplurality of factors may further comprise an expected scrap rate forcontainers produced by the blow molder until the containers reach thedesired container characteristic. Also, the operating cost data for theblow molder may comprise incremental costs to make changes to theoperating parameters of the blow molder, where the incremental costs arebased, at least in part, on the energy costs for operating the blowmolder.

In various implementations, the blow molder controller determines theset of blow molder input parameters by performing steps that comprise:(a) determining multiple sets of blow molder input parameters, whereineach of the multiple sets of blow molder input parameters drives thecontainers generated by the blow molder toward the desired containercharacteristic; (b) determining an incremental cost associated with eachof the multiple sets of blow molder input parameters; and (c) selectinga first set of blow molder input parameters from the multiple sets ofblow molder input parameters based on the incremental costs associatedwith each of the multiple sets of blow molder input parameters. Theincremental costs can be determined based on current operatingparameters of the blow molder and the energy costs for the blow molder,where the current operating parameters of the blow molder are sensed, atleast in part, by the blow molder sensor.

In various implementations, the set of blow molder input parameterscomprises a change to at least one of the following operating parametersof the blow molder: pre-blow timing; pre-blow pressure; power levels forindividual heater elements of the plurality of molds; preformtemperature set points; stretch rod timing; and/or blow pressure.

In various implementations, the container inspection system comprises amaterial distribution sensor system for sensing a material distributioncharacteristic of the containers. The material distribution sensorsystem may comprise one or more emitter-detector pairs, where theemitter emits light energy toward the containers and the detectorsdetect light energy that passes through a sidewall (e.g., through twosidewalls) of the containers. In various implementations, the containerinspection system may comprise a crystallinity sensor for sensing acrystallinity level of the containers. The crystallinity sensor maycomprise a camera, a birefringence sensor or a NIR spectroscopy sensor.The blow molder sensor may comprise an oven temperature sensor;individual mold temperature sensors for the plurality of molds; and/or ablow pressure sensor. The desired container characteristic may include adesired container sidewall thickness and/or a desired crystallinitylevel. The energy costs may include electricity costs for a plant wherethe blow molder is located. Also, the blow molder blows a fluid, such asair or liquid, into the preforms to form the containers.

The examples presented herein are intended to illustrate potential andspecific implementations of the embodiments. It can be appreciated thatthe exemplary embodiments are intended primarily for purposes ofillustration for those skilled in the art. No particular aspect oraspects of the examples is/are intended to limit the scope of thedescribed embodiments.

As used in the claims, the terms “container(s)” and “plasticcontainer(s)” mean any type of blow-molded container made from any typeof plastic material including, polyethylene terephthlat (PET), orientedpolypropolyene (OPP), etc.

FIG. 15 is a block diagram illustrating a computing device hardwarearchitecture 1500, within which a set or sequence of instructions can beexecuted to cause a machine to perform examples of any one of themethodologies discussed herein. For example, the blow molder controller102 may be executed on a computing device having an architecture similarto the architecture 1500. The architecture 1500 may describe, forexample, any of the computing devices described herein. The architecture1500 may execute the software architecture 702 described with respect toFIG. 7 . The architecture 1500 may operate as a standalone device or maybe connected (e.g., networked) to other machines. In a networkeddeployment, the architecture 1500 may operate in the capacity of eithera server or a client machine in server-client network environments, orit may act as a peer machine in peer-to-peer (or distributed) networkenvironments. The architecture 1500 can be implemented in a personalcomputer (PC), a tablet PC, a hybrid tablet, a set-top box (STB), apersonal digital assistant (PDA), a mobile telephone, a web appliance, anetwork router, a network switch, a network bridge, or any machinecapable of executing instructions (sequential or otherwise) that specifyoperations to be taken by that machine.

The example architecture 1500 includes a processor unit 1502 comprisingat least one processor (e.g., a central processing unit (CPU), agraphics processing unit (GPU), or both, processor cores, compute nodes,etc.). The architecture 1500 may further comprise a main memory 1504 anda static memory 1506, which communicate with each other via a link 1508(e.g., bus). The architecture 1500 can further include a video displayunit 1510, an alphanumeric input device 1512 (e.g., a keyboard), and aUI navigation device 1514 (e.g., a mouse). In some examples, the videodisplay unit 1510, alphanumeric input device 1512, and UI navigationdevice 1514 are incorporated into a touchscreen display. Thearchitecture 1500 may additionally include a storage device 1516 (e.g.,a drive unit), a signal generation device 1518 (e.g., a speaker), anetwork interface device 1520, and one or more sensors (not shown), suchas a GPS sensor, compass, accelerometer, or other sensor.

In some examples, the processor unit 1502 or another suitable hardwarecomponent may support a hardware interrupt. In response to a hardwareinterrupt, the processor unit 1502 may pause its processing and executean ISR, for example, as described herein.

The storage device 1516 includes a machine-readable medium 1522 on whichis stored one or more sets of data structures and instructions 1524(e.g., software) embodying or utilized by any one or more of themethodologies or functions described herein. The instructions 1524 canalso reside, completely or at least partially, within the main memory1504, within the static memory 1506, and/or within the processor unit1502 during execution thereof by the architecture 1500, with the mainmemory 1504, the static memory 1506, and the processor unit 1502 alsoconstituting machine-readable media. The instructions 1524 stored at themachine-readable medium 1522 may include, for example, instructions forexecuting any of the features described herein, etc.

While the machine-readable medium 1522 is illustrated in an example tobe a single medium, the term “machine-readable medium” can include asingle medium or multiple media (e.g., a centralized or distributeddatabase, and/or associated caches and servers) that store the one ormore instructions 1524. The term “machine-readable medium” shall also betaken to include any tangible medium that is capable of storing,encoding, or carrying instructions for execution by the machine and thatcause the machine to perform any one or more of the methodologies of thepresent disclosure, or that is capable of storing, encoding, or carryingdata structures utilized by or associated with such instructions. Theterm “machine-readable medium” shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia. Specific examples of machine-readable media include non-volatilememory, including, but not limited to, by way of example, semiconductormemory devices (e.g., electrically programmable read-only memory (EPROM)and electrically erasable programmable read-only memory (EEPROM)) andflash memory devices; magnetic disks such as internal hard disks andremovable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

The instructions 1524 can further be transmitted or received over acommunications network 1526 using a transmission medium via the networkinterface device 1520 utilizing any one of a number of well-knowntransfer protocols (e.g., hypertext transfer protocol (HTTP)). Examplesof communication networks include a LAN, a WAN, the Internet, mobiletelephone networks, plain old telephone service (POTS) networks, andwireless data networks (e.g., Wi-Fi, 3G, and 5G LTE/LTE-A or WiMAXnetworks). The term “transmission medium” shall be taken to include anyintangible medium that is capable of storing, encoding, or carryinginstructions for execution by the machine, and includes digital oranalog communications signals or other intangible media to facilitatecommunication of such software.

Various components are described in the present disclosure as beingconfigured in a particular way. A component may be configured in anysuitable manner. For example, a component that is or that includes acomputing device may be configured with suitable software instructionsthat program the computing device. A component may also be configured byvirtue of its hardware arrangement or in any other suitable manner.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described examples (or one or moreaspects thereof) can be used in combination with others. Otherembodiments can be used, such as by one of ordinary skill in the artupon reviewing the above description. The Abstract is to allow thereader to quickly ascertain the nature of the technical disclosure, forexample, to comply with 37 C.F.R. § 1.72(b) in the United States ofAmerica. It is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims.

Also, in the above Detailed Description, various features can be groupedtogether to streamline the disclosure. However, the claims cannot setforth every feature disclosed herein, as embodiments can feature asubset of said features. Further, embodiments can include fewer featuresthan those disclosed in a particular example. Thus, the following claimsare hereby incorporated into the Detailed Description, with each claimstanding on its own as a separate embodiment. The scope of theembodiments disclosed herein is to be determined with reference to theappended claims, along with the full scope of equivalents to which suchclaims are entitled.

What is claimed is:
 1. A blow molder system comprising: a blow molderfor performing a blow molding process, wherein the blow moldercomprises: a plurality of molds; and a blow molder sensor for sensing anoperating condition of the blow molder; an inspection system forinspecting plastic objects involved in the blow molding process; anambient temperature sensor for sensing an ambient temperature of a plantin which the blow molder is located; and a blow molder controller thatis in communication with the blow molder and the inspection system,wherein the blow molder controller is configured to: determine a set ofblow molder input parameters for the blow molder that drives the blowmolding process toward a desired characteristic, wherein the set of blowmolder input parameters are determined based on: output from theinspection system; output from the blow molder sensor; and output fromthe ambient temperature sensor; and output the set of blow molder inputparameters to the blow molder for implementation by the blow molder. 2.The blow molder system of claim 1, further comprise a moisture sensorfor sensing a moisture level of a plant in which the blow molder islocated, wherein the blow molder controller is further configured todetermine the set of blow mold parameters based on moisture level datafrom the plant moisture sensor.
 3. The blow molder system of claim 1,wherein the blow molder controller is further configured to determinethe set of blow molder parameters based on operating cost data for theblow molder.
 4. The blow molder system of claim 3, wherein the operatingcost data comprises energy costs for operating the blow molder.
 5. Theblow molder system of claim 4, wherein the blow molder controller isfurther configured to determine the set of blow molder parameters byoptimizing a plurality of factors, wherein the plurality of factorscomprise: satisfaction of the desired characteristic; and operatingcosts to implement the set of blow molder input parameters.
 6. The blowmolder system of claim 5, wherein: the blow molder produces plasticcontainers from plastic preforms; and the desired characteristiccomprises a desired characteristic of the containers produced by theblow molder.
 7. The blow molder system of claim 6, wherein the plasticobjects comprise the plastic containers produced by the blow molder. 8.The blow molder system of claim 6, wherein the plurality of factorsfurther comprise an expected scrap rate for containers produced by theblow molder until the containers reach the desired containercharacteristic.
 9. The blow molder system of claim 8, wherein the set ofblow molder input parameters comprises a change to at least one of thefollowing operating parameters of the blow molder: pre-blow timing;pre-blow pressure; power levels for individual heater elements of theplurality of molds; preform temperature set points; stretch rod timing;and blow pressure.
 10. The blow molder system of claim 7, wherein theenergy costs comprise electricity costs for a plant where the blowmolder is located.
 11. The blow molder system of claim 10, wherein theblow molder controller determines the set of blow molder inputparameters by performing steps that comprise: determining multiple setsof blow molder input parameters, wherein each of the multiple sets ofblow molder input parameters drives the containers generated by the blowmolder toward the desired container characteristic; and determining anincremental cost associated with each of the multiple sets of blowmolder input parameters, wherein the incremental costs are determinedbased on current operating parameters of the blow molder and the energycosts for the blow molder, wherein the current operating parameters ofthe blow molder are sensed, at least in part, by the blow molder sensor;and selecting a first set of blow molder input parameters from themultiple sets of blow molder input parameters based on the incrementalcosts associated with each of the multiple sets of blow molder inputparameters.
 12. The blow molder system of claim 4, wherein the operatingcost data comprises incremental costs to make changes to the blow molderinput parameters of the blow molder, wherein the incremental costs arebased, at least in part, on the energy costs for operating the blowmolder.
 13. The blow molder system of claim 1, wherein the set of blowmolder input parameters comprises a change to at least one of thefollowing operating parameters of the blow molder: pre-blow timing;pre-blow pressure; power levels for individual heater elements of theplurality of molds; preform temperature set points; stretch rod timing;and blow pressure.
 14. The blow molder system of claim 1, wherein theinspection system comprises a material distribution sensor system forsensing a material distribution characteristic of the plastic objects.15. The blow molder system of claim 1, wherein the inspection systemcomprises a camera.
 16. The blow molder system of claim 1, wherein theblow molder sensor comprises a sensor selected from the group consistingof: an oven temperature sensor; individual mold temperature sensors forthe plurality of molds; and a blow pressure sensor.
 17. The blow moldersystem of claim 6, wherein the blow molder blows a fluid into thepreforms to form the containers.
 18. The blow molder system of claim 17,wherein the fluid comprises air or a liquid.
 19. The blow molder systemof claim 10, wherein: the blow molder produces plastic containers fromplastic preforms; the desired characteristic comprises a desiredcharacteristic of the containers produced by the blow molder; and theplastic objects comprise the plastic containers produced by the blowmolder.
 20. The blow molder system of claim 19, wherein the inspectionsystem comprises at least one emitter-detector pair, wherein an emitterof the emitter-detector pair emits light energy and a detector of thelight energy pair detects light energy.
 21. The blow molder system ofclaim 20, wherein the at least one emitter-detector pair comprises aplurality of emitter-detector pairs, wherein the emitter of eachemitter-detector pair emits light energy toward the containers and thedetector of each emitter-detector pair senses light energy that passesthrough at least one sidewall of the containers.
 22. The blow moldersystem of claim 1, wherein the plastic objects comprise polyethyleneterephthalate.
 23. The blow molder system of claim 7, wherein theoperating cost data comprise a cost of expected scrap containersproduced by the blow molder.
 24. The blow molder system of claim 23,wherein the cost of expected scrap containers is determined, in part,based on a time for the blow molder to produce containers that satisfythe desired container characteristic.
 25. The blow molder system ofclaim 7, wherein the plurality of factors that the blow moldercontroller optimizes to determine the set of blow molder containersfurther comprises a time for the blow molder to produce containers thatsatisfy the desired container characteristic.
 26. The blow molder systemof claim 2, wherein: the blow molder controller is further configured todetermine the set of blow molder parameters based on operating cost datafor the blow molder; the operating cost data comprises energy costs foroperating the blow molder; the blow molder controller is furtherconfigured to determine the set of blow molder parameters by optimizinga plurality of factors, wherein the plurality of factors comprise:satisfaction of the desired characteristic; and operating costs toimplement the set of blow molder input parameters.
 27. The blow moldersystem of claim 26, wherein: the blow molder produces plastic containersfrom plastic preforms; and the desired characteristic comprises adesired characteristic of the containers produced by the blow molder.28. The blow molder system of claim 27, wherein the plurality of factorsfurther comprise an expected scrap rate for containers produced by theblow molder until the containers reach the desired containercharacteristic.
 29. The blow molder system of claim 28, wherein the setof blow molder input parameters comprises a change to at least one ofthe following operating parameters of the blow molder: pre-blow timing;pre-blow pressure; power levels for individual heater elements of theplurality of molds; preform temperature set points; stretch rod timing;and blow pressure.
 30. A blow molder system comprising: a blow molderfor performing a blow molding process, wherein the blow moldercomprises: a plurality of molds; and a blow molder sensor for sensing anoperating condition of the blow molder; an inspection system forinspecting plastic objects involved in the blow molding process; amoisture sensor for sensing a moisture level of a plant in which theblow molder is located; and a blow molder controller that is incommunication with the blow molder and the inspection system, whereinthe blow molder controller is configured to: determine a set of blowmolder input parameters for the blow molder that drives the blow moldingprocess toward a desired characteristic, wherein the set of blow molderinput parameters are determined based on: output from the inspectionsystem; output from the blow molder sensor; and output from the moisturesensor; and output the set of blow molder input parameters to the blowmolder for implementation by the blow molder.
 31. The blow molder systemof claim 30, wherein: the blow molder controller is further configuredto determine the set of blow molder parameters based on operating costdata for the blow molder; the operating cost data comprises energy costsfor operating the blow molder; the blow molder controller is furtherconfigured to determine the set of blow molder parameters by optimizinga plurality of factors, wherein the plurality of factors comprise:satisfaction of the desired characteristic; and operating costs toimplement the set of blow molder input parameters.
 32. The blow moldersystem of claim 31, wherein: the blow molder produces plastic containersfrom plastic preforms; and the desired characteristic comprises adesired characteristic of the containers produced by the blow molder.33. The blow molder system of claim 32, wherein the plurality of factorsfurther comprise an expected scrap rate for containers produced by theblow molder until the containers reach the desired containercharacteristic.
 34. The blow molder system of claim 33, wherein the setof blow molder input parameters comprises a change to at least one ofthe following operating parameters of the blow molder: pre-blow timing;pre-blow pressure; power levels for individual heater elements of theplurality of molds; preform temperature set points; stretch rod timing;and blow pressure.